Our PN network is based on PointNet [1]}, as illustrated in Figure REF .
The PointNet architecture is invariant to all \(n!\)  possible permutations of the input point cloud, and hence an ideal structure for processing raw point clouds.
The invariance is achieved by processing all points independently using multi-layer perceptrons (MLPs) with shared weights.
The obtained feature vectors are finally max-pooled to create a global feature representation of the input point cloud.
Finally, we attach a three-layer regression MLP on top of this global feature to predict the rotation.
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